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Bayesian models of cognition frame the mind as a probabilistic inference engine. This approach posits that the brain represents knowledge as probability distributions and updates these beliefs upon receiving new evidence according to Bayes’ theorem. It models perception, learning, and reasoning as optimal or near-optimal statistical inference under uncertainty, providing a unifying mathematical framework for many cognitive functions.
该方法的核心是贝叶斯规则：P(H|D) = P(D|H)P(H)/P(D)，其中H代表假设，D代表观测数据。后验概率P(H|D)（观测数据后对假设的信念）与似然性P(D|H)（假设对数据的解释程度）和先验概率P(H)（对假设的初始信念）的乘积成正比。该框架为理性主体如何更新其信念提供了一个规范标准。
In cognitive science, this is applied by assuming that the mind implicitly performs these calculations. For example, in perception, the brain combines noisy sensory input (the data) with prior knowledge about the world to form a stable percept (the posterior). This can explain many visual illusions, where prior expectations override sensory data. In language learning, a child might use 贝叶斯推断 to figure out the meaning of a new word by considering which potential meaning best explains the contexts in which the word was used. The approach is powerful because it provides a unifying mathematical framework for diverse cognitive phenomena and connects cognition directly to statistics and machine learning.
认知贝叶斯模型
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